HAMDA: Hybrid Approach for MiRNA-Disease Association prediction

Journal of Biomedical Informatics
Xing ChenGui-Ying Yan

Abstract

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA als...Continue Reading

Citations

Jul 30, 2019·Frontiers in Microbiology·Ya-Wei NiuGui-Ying Yan
Dec 21, 2019·Journal of Biomedical Informatics·Jihwan HaSanghyun Park
Jan 7, 2020·Journal of Biomedical Informatics·Haodi ZhongRobert Gwadera
Apr 23, 2021·BMC Medical Informatics and Decision Making·Yu-Tian WangChun-Hou Zheng
Apr 27, 2021·Frontiers in Genetics·Mingyan TangLei Deng
Aug 29, 2020·Journal of Biomedical Informatics·Renji P CherianSunder Venkitachalam

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